ABOUT THE HKS AUTHOR
Lauren Brodsky is the Senior Director of the HKS Communications Program and a Lecturer in Public Policy. She teaches courses on policy writing and persuasive communications, and is the Faculty Chair of the executive education program, “Persuasive Communication: Narrative, Evidence, Impact.”
Lauren’s writing has appeared in Harvard Business Review and Fast Company, and she is a co-author of Because Data Can’t Speak for Itself, published by Johns Hopkins University Press, 2023. She also publishes the website Policy Memo Resource (www.policymemos.hks.harvard.edu) at HKS.
Lauren lectures widely on policy communications and the use of evidence in writing for governmental agencies and non-profit organizations.
Before coming to HKS, Lauren taught courses on communications and international relations at Northeastern University, Tufts University, SUNY Albany, and Skidmore College. She is a former Theodore Sorensen Research Fellow at the John F. Kennedy Presidential Library and Museum, where she conducted archival research on public diplomacy programs during the Kennedy administration.
Lauren holds a B.A. from the University of Pennsylvania and an M.A.L.D. and Ph.D. from the Fletcher School at Tufts University.
People with important evidence-based ideas often struggle to translate data into stories their readers can relate to and understand. And if leaders can’t communicate well to their audience, they will not be able to make important changes in the world. Why do some evidence-based ideas thrive while others die? And how do we improve the chances of worthy ideas? In Because Data Can’t Speak for Itself, accomplished educators and writers David Chrisinger and Lauren Brodsky tackle these questions head-on. They reveal the parts and functions of effective data-driven stories and explain myriad ways to turn your data dump into a narrative that can inform, persuade, and inspire action. Chrisinger and Brodsky show that convincing data-driven stories draw their power from the same three traits, which they call people, purpose, and persistence. Writers need to find the real people behind the numbers and share their stories. At the same time, they need to remember their own purpose and be honest about what data says—and, just as importantly, what it does not. Compelling and concise, this fast-paced tour of success stories—and several failures—includes examples on topics such as COVID-19, public diplomacy, and criminal justice. Chrisinger and Brodsky’s easy-to-apply tool kit will turn anyone into an effective and persuasive evidence-based writer. Aimed at policy analysts, politicians, journalists, teachers, and business leaders, Because Data Can’t Speak for Itself will transform the way you communicate ideas.
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Alessandra Seiter: The Great Recession of 2008 was a defining moment in U.S. history, in no small part thanks to record high unemployment. A newly inaugurated President Obama swiftly enacted a landmark stimulus package designed to save and create as many jobs as possible. But many of the workers who lost their jobs at the beginning of the Great Recession never found steady work again.
Most of these individuals were people over the age of 55 who were still able to work. Hoping to better understand and improve the situation, in 2012, the Government Accountability Office, or the GAO, released a report that encouraged Secretary of Labor Hilda Solis to take action. And take action she did, awarding nearly $200 million in grants to support older workers experiencing long term unemployment.
The GAO report contained a lot of numbers, from household income to the number of available jobs. But the numbers themselves didn’t spur the Department of Labor to action, at least according to Lauren Brodsky of Harvard Kennedy School and David Chrisinger of the University of Chicago, Harris School of Public Policy.
Brodsky and Chrisinger both teach public policy students how to craft effective policy communications. And they argue that the jobs report on older workers was so persuasive not because it had a lot of statistics, but because it had a lot of compelling stories.
On this episode of Behind the Book, we speak with Lauren Brodsky about her and David Chrisinger’s new book, Because Data Can’t Speak for Itself: A Practical Guide to Telling Persuasive Policy Stories.
The stories told in the GAO report are from older workers whose personal experiences couldn’t be captured by the unemployment rate. For example, one individual shared that his son actually moved back in with the family so he could pay rent each week to his parents because he felt bad for them. One woman shared that after she was laid off, she didn’t want to go to the doctor because she couldn’t afford treatment.
Lauren and David think this kind of storytelling, which elevates the experiences of real people impacted by social problems, is critical to effective policy communication. The first section of their book emphasizes that all data is grounded in people and that humanizing that data is more likely to spur policymakers to action.
Lauren Brodsky: If you think about impacting a decision maker, and trying to create the change that you want to see in the world, the numbers will only get you so far because that decision maker needs some sense of care before they’re going to act, and the care is going to come from people. So if you throw out a number about how many people are affected by a disease and you see number and you see disease, you lose humans and how they are affected in that data point, and it can create distance for your audience rather than empathy.
Seiter: By telling stories about people rather than about the data itself, Lauren and David argue that policy communicators can cut through the overwhelming amount of information available to decision makers and instead provide a compelling path forward.
To do this well, Lauren and David also encourage policy communicators to remember why they’re collecting and deploying data in the first place. If the goal of effective policy communication is to describe what is happening, identify effective interventions and demonstrate what should be done to address a problem, then maybe communicators don’t need to share every data point available to them.
Brodsky: Why am I communicating about this? What is the problem that I see in the world? What am I aiming to get others to care about and then fix? And if you stay that narrow, you won’t bring in extraneous data that you don’t need. What I find a lot with writers is they’re bringing in that data because they have it. And we’re so used to sharing what we know because we’re forgetting that the piece of communication is not about us. It’s for an audience.
Seiter: In the case of the GAO report on older workers, the goal was not to demonstrate how much data the Bureau of Labor Statistics collects, but to implement strategies that would better the lives of Americans experiencing the real world hardships of long term unemployment.
Lauren and David are quick to point out that even the most expertly crafted storytelling won’t guarantee the success of the strategies recommended. They emphasize that if the goal of data driven decision making is to solve problems, then we need to tell honest stories about both the problems and their possible solutions. This honesty involves not overstating the data’s meaning; being transparent about what you do and don’t know; and remembering that correlation does not equal causation.
On an institutional level, Lauren and David argue that policymakers need to be just as intentional about the data they collect as they are about how they communicate with it. For example, to date, the Government Accountability Office has not released an impact evaluation of the grants made by the Department of Labor, so we still aren’t sure about their role in the outcomes of older workers. Lauren and David think this focus on collecting data about problems at the expense of collecting data about solutions is all too common.
Brodsky: One thing we’re thinking about is helping organizations build evidence-gathering cultures not just on the problem, but also on how well is the solution going. And mistakes will be made. But if you’re gathering data on how well solutions are going and where solutions have gone well in other places, other organizations, you can bring that in to catch that type of a thing.
Seiter: By keeping people purpose and persistence in mind, Lauren and David argue that students, government agencies and policy communicators everywhere can use data to effect change.
The book is Because Data Can’t Speak For Itself: A Practical Guide to Telling Persuasive Policy Stories, written by Lauren Brodsky, lecturer in public policy at Harvard Kennedy School, and David Chrisinger, executive and writing director in the writing program at the University of Chicago, Harris School of Public Policy. It’s published by Johns Hopkins University Press.
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